#coding=utf-8

import tensorflow as tf
from tensorflow import keras
# import os

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt

fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels)  = fashion_mnist.load_data()
class_names = ['t恤', '裤子', '毛衣', '连衣裙', '外套',
               '拖鞋', '衬衫', '运动鞋', '袋', '短靴']

train_images = train_images / 255.0
test_images = test_images / 255.0

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_images, test_labels)

predictions = model.predict(test_images)

name = class_names[np.argmax(predictions[1])]

print('\n第二张图片是： ', name)

